Addressing power imbalance in research: exploring power in integrated knowledge translation health research
Bibliographic record
Abstract
Integrated knowledge translation (IKT) is a knowledge translation framework that focuses collaboration between researchers and knowledge users (KUs) to generate research findings. KUs can be policymakers, clinicians, or those with lived experience who partner with researchers. While advocated as an approach that democratizes research and reduces power imbalance between researchers and KUs, it is not known if the implementation of IKT by health researchers actively addresses power imbalances. The aim of this study was to review research using an integrated knowledge translation approach to explore how power is addressed within these research studies. By looking broadly at how the studies addressed/described/discussed/dismantled power we explored examples of when this was done well and not so well, exposing the assumptions sometimes made by researchers. We drew from systematic review procedures combined with a modified critical discourse analysis (CDA) lens. We searched Medline, PsycINFO, CINAHL, Scopus, Social Science Database, SocIndex and Google Scholar for English language studies that focused on IKT and power. Data were extracted on study characteristics and a modified CDA which included questions in relation to power (e.g., description of power, phrases used to describe power, evidence of power dynamics, strategies for addressing power imbalances) and end user engagement (e.g., Did they ask KUs how they wanted to be involved? Did they engage in reflection with KUs? Did they discuss dissemination strategies with KUs). Eleven studies were eligible after screening 381 titles and reviewing 40 full-text studies. The use of IKT to address power varied significantly, revealing both positive examples as well as some missed opportunities to address power imbalances from study inception to dissemination. Revisiting the use of IKT to examine how power is defined, shared, and managed in relationships with KUs could provide valuable insights. Using a CDA framework to explore these dynamics would indeed address the nuances of power in research contexts. Future research should focus on developing strategies to effectively implement IKT to address power imbalances, leading to research that has a better chance of being useful, usable and used in practice. One of the difficulties of doing research is understanding and managing the power difference between researchers and knowledge users (community members/those impacted by disease/service providers). When power imbalances are not managed well in research teams, the results may not be as beneficial to its knowledge users because it may not be relevant, and further, power imbalance can negatively impact knowledge users’ experiences of engaging in research. Some researchers are trying to ensure there is more equality in research and explore how to address power differences within their own work. There are different ways to help researchers collaborate with knowledge users. One method of working with knowledge users in research is called Integrated Knowledge Translation (IKT), which started in Canada in the 1990s. IKT involves everyone in the research process working together from the start and is focused on ensuring that those who will be using research also inform its production. The aim of the current study was to review studies using an integrated knowledge translation approach to explore how power is addressed within these research studies. By broadly examining how the studies addressed, described, discussed, and dismantled power, we were able to identify various examples of effective and ineffective approaches. In the eleven papers we assessed, power was not always addressed or explained well. When it was, discussion about power was found in the background of the paper, or in some sections of their work rather than a strong focus of the work, suggesting there are many opportunities for researchers to better address power.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.239 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.015 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".